Reciprocity coordinates a three-way trade-off in recurrent neural circuits
Abstract
Biological neural circuits contain both unidirectional and reciprocal connections, but how reciprocity affects circuit computation remains unclear. We varied reciprocity \r{ho} across 21 levels in recurrent-network simulations while holding network size, connection density, and directed-edge count fixed. Low-reciprocity networks produced more diverse stimulus-driven activity and performed better on four continuous-input tasks, whereas higher reciprocity generally improved performance on three delayed-memory tasks. Increasing reciprocity reduced online memory capacity and accelerated recovery from perturbations. A post-cue noise experiment showed that higher reciprocity suppressed internal noise propagation but made cue-evoked states less distinguishable, and component matching separated the contributions of cue separation and within-cue dispersion to readout robustness. Within this ensemble, reciprocity thus mediates a three-way trade-off among representational capacity, local stability, and task-state separability: higher reciprocity lowers capacity, strengthens stability, and compresses separability. Whether it improves behaviour depends on whether the stability gain outweighs the lost separation between task states. Connectome and microcircuit datasets from five species showed intermediate reciprocity, while reciprocity varied substantially across adult Drosophila brain regions, providing compatible structural context and motivating circuit-specific functional tests.
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BibTeX
@misc{zhang2026reciprocity,
title = {{Reciprocity coordinates a three-way trade-off in recurrent neural circuits}},
author = {Xiaoyu Zhang and Mingyi Huang and Yixuan Cui and Xin Lu and Yuguo Yu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.19949},
eprint = {2610.19949},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.19949},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Reciprocity coordinates a three-way trade-off in recurrent neural circuits
AU - Xiaoyu Zhang
AU - Mingyi Huang
AU - Yixuan Cui
AU - Xin Lu
AU - Yuguo Yu
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.19949
UR - https://arxiv.org/abs/2610.19949
AB - Biological neural circuits contain both unidirectional and reciprocal connections, but how reciprocity affects circuit computation remains unclear. We varied reciprocity \r{ho} across 21 levels in recurrent-network simulations while holding network size, connection density, and directed-edge count fixed. Low-reciprocity networks produced more diverse stimulus-driven activity and performed better on four continuous-input tasks, whereas higher reciprocity generally improved performance on three delayed-memory tasks. Increasing reciprocity reduced online memory capacity and accelerated recovery from perturbations. A post-cue noise experiment showed that higher reciprocity suppressed internal noise propagation but made cue-evoked states less distinguishable, and component matching separated the contributions of cue separation and within-cue dispersion to readout robustness. Within this ensemble, reciprocity thus mediates a three-way trade-off among representational capacity, local stability, and task-state separability: higher reciprocity lowers capacity, strengthens stability, and compresses separability. Whether it improves behaviour depends on whether the stability gain outweighs the lost separation between task states. Connectome and microcircuit datasets from five species showed intermediate reciprocity, while reciprocity varied substantially across adult Drosophila brain regions, providing compatible structural context and motivating circuit-specific functional tests.
ER -